ANALISIS METODE K-MEANS PADA PENGELOMPOKAN PERGURUAN TINGGI MENURUT PROVINSI BERDASARKAN FASILITAS YANG DIMILIKI DESA
Bibliographic record
Abstract
Higher education is an education level that includes diplomat, undergraduate and doctoral programs. The purpose of higher education is to improve the quality of the workforce, to help improve the quality of the workforce each university must have the facilities needed in teaching and learning activities. This study discusses the Analysis of the K-Means Method in the Grouping of Universities by Province Based on the Facilities of the Village. Sources of data obtained from data collected based on documents from 2003 to 2018 through the website of the Indonesian Statistics Agency. Data is processed into 2 clusters, namely the highest facility level cluster (C1) and the lowest facility level cluster (C2). So that obtained from 34 provinces 3 provinces are grouped in high facility level clusters (C1) and 31 provinces are grouped in low facility level clusters (C2). This can be input to the government for provinces that have higher education institutions that still have inadequate facilities in each village and are of more concern to the government based on the cluster that is being conducted.Keywords: K-Means, Higher education, Grouping, Facilities
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".